数量性状的核质互作遗传效应分析:二倍体植株遗传模型

提出了能分析二倍体植株数量性状核质互作效应的遗传模型,该模型把控制数量性状总的遗传效应分为核效应、质效应和核质互作效应,以及它们分别与环境作用的效应。其中,核质互作效应可进一步分解为加性核质互作与显性核质互作。基于平衡与非平衡两种双列杂交试验设计,蒙特卡罗模拟结果表明:采用混合线性模型方法进行统计分析,可以有效地估计各项遗传效应值及其方差分量。此外,运用该模型对棉花的4个数量性状(单株铃数、衣分、2.5%跨长和麦克隆值)进行了遗传分析。     

双交组合农艺性状的ADAA模型及其分析方法

根据双交组合方式的交配设计,运用Ｃｏｃｋｅｒｈａｍ广义模型的建模原理,提出了适用于分析作物农艺性状的加性－显性－上位性模型（ＡＤＡＡ模型）,推导了不同世代群体的遗传效应分量。采用不同世代对双交组合ＡＤＡＡ模型及其缩减的ＡＤ模型进行蒙特卡罗模拟比较,结果表明;采用ＭＩＮＱＵＥ（１）法可以无偏估计各遗传方差分量,采用ＡＵＰ法能够有效地预测各遗传效应值;分析ＡＤＡＡ模型的世代数以不少于３个（亲本、单本Ｆ     

半双列杂交和完全双列杂交遗传分析的比较

一、引言在一般试验中,我们采用诸如完全随机、随机区组等设计,目的在于控制环境的干扰以估算试验误差和提高试验精度。这通常称为环境设计。但在数量遗传试验中,除环境设计外,还必须根据试验目的和遗传原理进行适当的交配设计,以估算有关群体数量性状变异的遗传分量和环境分量,进而研究性状的遗传模式和为选择合适的育种方案提供理论依据。     

粳稻品质性状间及其与植株性状和产量性状间的遗传相关

利用朱军等提出的种子性状遗传模型,采用 3×3 NCⅡ正反交设计的亲本和部分组合F2代种子,分析了品质性状糙米率、垩白粒率、垩白面积和AC间及上述品质性状与株高、穗部性状等的遗传相关,以期为粳稻育种后代选择提供指导。结果表明,精米重与糙米率存在极显著的母体加性相关;虽然控制品质性状的主要遗传效应分量与植株性状相应遗传效应分量遗传协方差不显著,但在其他相应遗传效应分量方面存在着复杂的关系。     

非交叉配子形成体的连锁图谱构建方法

根据非交叉(achiasmatic)遗传模型,提出采用最大似然法计算遗传交换率的方法,同时开发了构建非交叉生物(F2群体)连锁图谱的计算机软件。通过卡方验检可测性连锁分子标记。对于无交叉生物现象,采用蒙特卡洛模拟技术,对交叉(chiasmatic)和非交叉两个遗传模型遗传交换率的估计值和作图效率进行了比较。模拟结果表明,非交叉模型能提供无偏的估计值,而交叉模型则只有实际值的一半。在所有同等的条件下,基于非交叉模型的作图效率均高于基于交叉模型(无校正)的作图效率。对于非交叉配子形成体,采用基于非交叉模型的交换率计算方法能获得理想的作图效率。     

作物杂种后代基因型值和杂种优势的预测方法

本文提出了利用作物亲本和F_1预测杂种后代基因型值和杂种优势的统计分析方法.该方法运用加性-显性遗传模型,分析双列杂交试验资料,用MINQUE(1)法估算方差分量以及预测遗传效应值.由加性和显性效应预测值可进一步预测F_1,F_2,BC_1,BC_2,等不同世代的基因型值,在预测F_1群体平均优势和群体超亲优势的基础上,可以推导出其它各世代的杂种优势.提出了预测杂种后代保持超亲优势世代数的简单公式,根据杂交组合F_1群体平均优势和双亲相对遗传差异,便可预测该组合能在生产上直接利用的世代数.以棉花六个品种完全双列杂交试验资料为例,分析了各组合F_1和F_2的基因型值、超亲优势和保持5％超亲优势的世代数.     

基于MODIS的中国草地NPP综合估算模型

草地生态系统是陆地生态系统分布最广的生态系统类型之一,其碳储量的估算在全球变化中的作用越来越受到重视。为了快速、便捷地实现中国草地净初级生产力(NPP)的估算,在获取野外调查资料与同期遥感影像数据的基础上,利用归一化植被指数(NDVI)以及气候数据,构建了草地NPP综合估算模型。模型包括叶面积指数(LAI)和光合累积量(PA)两个子模型,其中LAI子模型利用了遥感数据NDVI,PA子模型利用了温度、降水和辐射等气候数据。通过建模以外独立的实测数据的验证,模拟值与实测值之间有很好的相关性,R2为0.8519,相关性达到极显著水平。RMSE和RRMSE均较小,表明模型的模拟结果比较可靠。同时模拟值与实测值之间的平均相对误差仅为1.97%,模拟结果的准确度较高,因此利用上述模型估算中国草地NPP是可行的。以上结果为中国草地NPP估算提供了新的方法。     

远交群体动态性状基因定位的似然分析Ⅰ.理论方法

受动物遗传育种中用来估计动态性状育种值的随机回归测定日模型思想的启发 ,将关于时间 (测定日期 )的Legendre多项式镶嵌在遗传模型的每个遗传效应中 ,以刻画QTL对动态性状变化过程的作用 ,从而建立起动态性状基因定位的数学模型。利用远交设计群体 ,阐述了动态性状基因定位的似然分析原理 ,推导了定位参数似然估计的EM法两步求解过程。结合动态性状遗传分析的特点和普通数量性状基因定位研究进展 ,还提出了有关动态性状基因定位进一步研究的设想     

利用大型蒸渗仪模拟土壤-植物-大气连续体水分蒸散

在农田水量转化各分量中,蒸散与潜水蒸发是最难测定的。在地下水浅埋地区,地下水通过毛管上升而补给包气带土壤水的作用十分明显,对作物生长意义重大。利用大型蒸渗仪、波文比、水力蒸发器等仪器,获得了大量水平衡因子的试验数据和土壤植物大气连续体(SPAC)模型中的有关参数。以大型蒸渗仪实测值为基准,验证了农田土壤植物大气连续体模型的模拟值,并主要就蒸散和潜水蒸发量,对实测与模拟值作了比较分析,探讨了导致两者差异的原因。     

运用Monte Carlo模拟方法评价林木转化分析法

使用蒙特卡罗模拟(Monte Carlo simulation)方法来评价了林木非平衡单因素随机区组试验资料转化前后的分析效果.为了减少工作量,并使研究结果具有普遍性,采用了5个试验,单因素RCB设计,将多株小区转化成单株小区,研究转化分析法的统计学基础.评价非平衡试验资料转化前后分析法优劣的指标有,(1)有无负的方差分量;(2)参数的偏性,偏性的显著性和均方误大小;(3)试验资料的转化对家系遗传力和单株遗传力估计值大小和误差的影响.经过比较分析发现t(1)转化分析法可以消灭负的方差分量;(2)在参数的偏性、偏性的显著性和均方误大小方面,试验I至III的结果是一致的,除了未转化的资料Vb偏差达到显著水平外,其它参数间的偏差不显著;所有参数的均方误都是未转化的资料大;(3)对试验资料进行转化,有利于提高提高遗传力的大小,降低参数的误差.由于林木造林试验多采用4-8株小区,所以可以得出结论;转化分析法都要优于原模型分析法.建议在林木遗传育种实践中采用转化分析法来处理非平衡试验资料.     

Inferring the trajectory of genetic variance in the course of artificial selection

A method is proposed to infer genetic parameters within a cohort, using data from all individuals in an experiment. An application is the study of changes in additive genetic variance over generations, employing data from all generations. Inferences about the genetic variance in a given generation are based on its marginal posterior distribution, estimated via Markov chain Monte Carlo methods. As defined, the additive genetic variance within the group is directly related to the amount of selection response to be expected if parents are chosen within the group. Results from a simulated selection experiment are used to illustrate properties of the method. Four sets of data are analysed: directional selection with and without environmental trend, and random selection, with and without environmental trend. In all cases, posterior credibility intervals of size 95% assign relatively high density to values of the additive genetic variance and heritability in the neighbourhood of the true values. Properties and generalizations of the method are discussed.     

A Bayesian framework for comparative quantitative genetics

Bayesian approaches have been extensively used in animal breeding sciences, but similar approaches in the context of evolutionary quantitative genetics have been rare. We compared the performance of Bayesian and frequentist approaches in estimation of quantitative genetic parameters (viz. matrices of additive and dominance variances) in datasets typical of evolutionary studies and traits differing in their genetic architecture. Our results illustrate that it is difficult to disentangle the relative roles of different genetic components from small datasets, and that ignoring, e.g. dominance is likely to lead to biased estimates of additive variance. We suggest that a natural summary statistic for G-matrix comparisons can be obtained by examining how different the underlying multinormal probability distributions are, and illustrate our approach with data on the common frog (Rana temporaria). Furthermore, we derive a simple Monte Carlo method for computation of fraternity coefficients needed for the estimation of dominance variance, and use the pedigree of a natural Siberian jay (Perisoreus infaustus) population to illustrate that the commonly used approximate values can be substantially biased.     

Simulating Evolution by Gene Duplication

By considering the recent finding that unequal crossing over and other molecular interactions are contributing to the evolution of multigene families, a model of the origin of repetitive genes was studied by Monte Carlo simulations. Starting from a single gene copy, how genetic systems evolve was examined under unequal crossing over, random drift and natural selection. Both beneficial and deteriorating mutations were incorporated, and the latter were assumed to occur ten times more frequently than the former. Positive natural selection favors those chromosomes with more beneficial mutations in redundant copies than others in the population, but accumulation of deteriorating mutations (pseudogenes) have no effect on fitness so long as there remains a functional gene. The results imply the following: Positive natural selection is needed in order to acquire gene families with new functions. Without it, too many pseudogenes accumulate before attaining a functional gene family. There is a large fluctuation in the outcome even if parameters are the same. When unequal crossing over occurs more frequently, the system evolves more rapidly. It was also shown, under realistic values of parameters, that the genetic load for acquiring a new gene is not as large as J.B.S. Haldane suggested, but not so small as in a model in which a system for selection started from already redundant genes.     

A Monte Carlo method for combined segregation and linkage analysis.

We introduce a Monte Carlo approach to combined segregation and linkage analysis of a quantitative trait observed in an extended pedigree. In conjunction with the Monte Carlo method of likelihood-ratio evaluation proposed by Thompson and Guo, the method provides for estimation and hypothesis testing. The greatest attraction of this approach is its ability to handle complex genetic models and large pedigrees. Two examples illustrate the practicality of the method. One is of simulated data on a large pedigree; the other is a reanalysis of published data previously analyzed by other methods.     

MCMC multilocus lod scores: application of a new approach

On extended pedigrees with extensive missing data, the calculation of multilocus likelihoods for linkage analysis is often beyond the computational bounds of exact methods. Growing interest therefore surrounds the implementation of Monte Carlo estimation methods. In this paper, we demonstrate the speed and accuracy of a new Markov chain Monte Carlo method for the estimation of linkage likelihoods through an analysis of real data from a study of early-onset Alzheimer's disease. For those data sets where comparison with exact analysis is possible, we achieved up to a 100-fold increase in speed. Our approach is implemented in the program lm_bayes within the framework of the freely available MORGAN 2.6 package for Monte Carlo genetic analysis (http://www.stat.washington.edu/thompson/Genepi/MORGAN/Morgan.shtml).     

Testing for linkage under robust genetic models.

Robust genetic models are used to assess linkage between a quantitative trait and genetic variation at a specific locus using allele-sharing data. Little is known about the relative performance of different possible significance tests under these models. Under the robust variance components model approach there are several alternatives: standard Wald and likelihood ratio tests, a quasilikelihood Wald test, and a Monte Carlo test. This paper reports on the relative performance (significance level and power) of the robust sibling pair test and the different alternatives under the robust variance components model. Simulations show that (1) for a fixed sample size of nuclear families, the variance components model approach is more powerful than the robust sibling pair approach; (2) when the number of nuclear families is at least approximately 100 and heritability at the trait locus is moderate to high (>0.20) all tests based on the variance components model are equally effective; (3) when the number of nuclear families is less than approximately 100 or heritability at the trait locus is low (<0. 20), on balance, the Monte Carlo test provides the best power and is the most valid. The different testing procedures are applied to determine which are able to detect the known association between low density lipoprotein cholesterol and the common genotypes at the locus encoding apolipoprotein E. Results from this application show that the robust sibling pair method may be more effective in practice than that indicated by simulations.     

Analysis of cytoplasmic and maternal effects I. A genetic model for diploid plant seeds and animals

A genetic model for modified diallel crosses is proposed for estimating variance and covariance components of cytoplasmic, maternal additive and dominance effects, as well as direct additive and dominance effects. Monte Carlo simulations were conducted to compare the efficiencies of minimum norm quadratic unbiased estimation (MINQUE) methods. For both balanced and unbalanced mating designs, MINQUE (0/1), which has 0 for all the prior covariances and 1 for all the prior variances, has similar efficiency to MINQUE(), which has parameter values for the prior values. Unbiased estimates of variance and covariance components and their sampling variances could be obtained with MINQUE(0/1) and jackknifing. A t-test following jackknifing is applicable to test hypotheses for zero variance and covariance components. The genetic model is robust for estimating variance and covariance components under several situations of no specific effects. A MINQUE(0/1) procedure is suggested for unbiased estimation of covariance components between two traits with equal design matrices. Methods of unbiased prediction for random genetic effects are discussed. A linear unbiased prediction (LUP) method is shown to be efficient for the genetic model. An example is given for a demonstration of estimating variance and covariance components and predicting genetic effects.     

A Thurstonian model for quantitative genetic analysis of ranks: a Bayesian approach

A fully Bayesian method for quantitative genetic analysis of data consisting of ranks of, e.g., genotypes, scored at a series of events or experiments is presented. The model postulates a latent structure, with an underlying variable realized for each genotype or individual involved in the event. The rank observed is assumed to reflect the order of the values of the unobserved variables, i.e., the classical Thurstonian model of psychometrics. Parameters driving the Bayesian hierarchical model include effects of covariates, additive genetic effects, permanent environmental deviations, and components of variance. A Markov chain Monte Carlo implementation based on the Gibbs sampler is described, and procedures for inferring the probability of yet to be observed future rankings are outlined. Part of the model is rendered nonparametric by introducing a Dirichlet process prior for the distribution of permanent environmental effects. This can lead to potential identification of clusters of such effects, which, in some competitions such as horse races, may reflect forms of undeclared preferential treatment.     

Genetic analysis of growth curves using the SAEM algorithm

The analysis of nonlinear function-valued characters is very important in genetic studies, especially for growth traits of agricultural and laboratory species. Inference in nonlinear mixed effects models is, however, quite complex and is usually based on likelihood approximations or Bayesian methods. The aim of this paper was to present an efficient stochastic EM procedure, namely the SAEM algorithm, which is much faster to converge than the classical Monte Carlo EM algorithm and Bayesian estimation procedures, does not require specification of prior distributions and is quite robust to the choice of starting values. The key idea is to recycle the simulated values from one iteration to the next in the EM algorithm, which considerably accelerates the convergence. A simulation study is presented which confirms the advantages of this estimation procedure in the case of a genetic analysis. The SAEM algorithm was applied to real data sets on growth measurements in beef cattle and in chickens. The proposed estimation procedure, as the classical Monte Carlo EM algorithm, provides significance tests on the parameters and likelihood based model comparison criteria to compare the nonlinear models with other longitudinal methods.     

Generalized character process models: estimating the genetic basis of traits that cannot be observed and that change with age or environmental conditions

The genetic analysis of characters that change as a function of some independent and continuous variable has received increasing attention in the biological and statistical literature. Previous work in this area has focused on the analysis of normally distributed characters that are directly observed. We propose a framework for the development and specification of models for a quantitative genetic analysis of function-valued characters that are not directly observed, such as genetic variation in age-specific mortality rates or complex threshold characters. We employ a hybrid Markov chain Monte Carlo algorithm involving a Monte Carlo EM algorithm coupled with a Markov chain approximation to the likelihood, which is quite robust and provides accurate estimates of the parameters in our models. The methods are investigated using simulated data and are applied to a large data set measuring mortality rates in the fruit fly, Drosophila melanogaster.